System and method of vehicle aware gesture recognition in vehicles with smart helmets

ABSTRACT

A helmet includes a transceiver configured to receive vehicle data from one or more sensors located on a vehicle, an inertial measurement unit (IMU) configured to collect helmet motion data of the helmet associated with a rider of the vehicle, and a processor in communication with the transceiver and IMU, and programmed to receive, via the transceiver, vehicle data from the one or more sensors located on the vehicle, determine a gesture in response to the vehicle data from the one or more sensors located on the vehicle and the helmet motion data from the IMU, and output on a display of the helmet a status interface related to the vehicle, in response to the gesture.

TECHNICAL FIELD

The present disclosure relates to intelligent helmets, such as thoseutilized on motorcycles or other vehicles including dirt bikes,three-wheeler vehicles, or four-wheeler vehicles such as an all-terrainvehicle or the like.

BACKGROUND

Smart helmets may be utilized by two-wheeler riders to provideinformation regarding head mounted see-through displays. The helmets mayprovide an unobstructed view of the world while overlaying alerts,notifications, and status information for the user or rider. Interactionwith such information may be essential for immersive user experience.Current helmets allow for speech interaction to be used to navigate andselect the display elements. However, while the rider is traveling athigh speed, speech interaction can be comparatively slow and limited. Inmost cases, the speech interaction may be multiplexed with riderintercom that impose limitations on the functionality. Additionally,speech interaction may impose a higher cognitive load on the rider incomparison to other interaction modalities. There may be benefits tocomplementing speech interaction with additional interaction modalities.

SUMMARY

According to one embodiment, a helmet includes a transceiver configuredto receive vehicle data from one or more sensors located on a vehicle,an inertial measurement unit (IMU) configured to collect motion data ofthe helmet worn by the rider of the vehicle, and a processor incommunication with the transceiver and IMU, and programmed to receive,via the transceiver, vehicle data from the one or more sensors locatedon the vehicle, determine a gesture in response to the vehicle data fromthe one or more sensors located on the vehicle and the helmet motiondata from the IMU, and output on a display of the helmet a statusinterface related to the vehicle, in response to the gesture.

According to a second embodiment, A system that includes a helmet and avehicle with at least two wheels includes a helmet transceiverconfigured to receive vehicle data from one or more sensors located onthe vehicle, a helmet inertial measurement unit (IMU) configured tocollect helmet motion data associated with the helmet, a rider-facingcamera located on the vehicle and configured to monitor a rider of thevehicle and collect rider image data, and a processor in the helmet incommunication with the helmet transceiver and helmet IMU. The processoris further programmed to receive, via the transceiver, gesture data fromthe helmet transceiver and vehicle motion data from a vehicle IMU,determine a gesture in response to the gesture data and compensate itwith the vehicle motion data, and output on display of a helmet anaction in response to the gesture.

According to a third embodiment, a helmet includes a transceiverconfigured to receive vehicle data from one or more sensors located on avehicle, an inertial measurement unit (IMU) configured to collect helmetmotion data of the helmet associated with a rider of the vehicle, and aprocessor in communication with the transceiver and IMU, and programmedto receive, via the transceiver, vehicle data from the one or moresensors located on the vehicle determine a gesture in response to thevehicle data from the one or more sensors located on the vehicle and thehelmet motion data from the IMU, and execute a command in response tothe gesture.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an example of a system design 100 that includes a smart helmet101 and a motorcycle 103.

FIG. 2 is an example of a system design for a smart helmet andmotorcycle that allows for gesture recognition.

FIG. 3 is an example flow chart of a smart helmet that allows gesturerecognition.

DETAILED DESCRIPTION

Embodiments of the present disclosure are described herein. It is to beunderstood, however, that the disclosed embodiments are merely examplesand other embodiments can take various and alternative forms. Thefigures are not necessarily to scale; some features could be exaggeratedor minimized to show details of particular components. Therefore,specific structural and functional details disclosed herein are not tobe interpreted as limiting, but merely as a representative basis forteaching one skilled in the art to variously employ the embodiments. Asthose of ordinary skill in the art will understand, various featuresillustrated and described with reference to any one of the figures canbe combined with features illustrated in one or more other figures toproduce embodiments that are not explicitly illustrated or described.The combinations of features illustrated provide representativeembodiments for typical applications. Various combinations andmodifications of the features consistent with the teachings of thisdisclosure, however, could be desired for particular applications orimplementations.

This disclosure makes references to helmets and saddle-ride vehicles. Itshould be understood that a “saddle-ride vehicle” typically refers to amotorcycle, but can include any type of automotive vehicle in which thedriver typically sits on a saddle, and in which helmets are typicallyworn due to absence of cabin to protect the rider. Other than amotorcycle, this can also include powered two-wheeler (PTW) vehiclessuch as dirt bikes, scooters, and the like. This can also include apowered three-wheeler, or a powered four-wheeler such as an all-terrainvehicle (ATV) and the like. Any references specifically to a motorcycleor bike can also apply to any other saddle-ride vehicle, unless notedotherwise.

A system may be utilized for recognizing head gestures on a moving twowheeler (or more vehicle) for interacting with a smart helmet display.The detected gestures may be used to control the display layout on thesmart helmet. Common head gestures such as nodding, pitching, lookup,yaw, and head tilt, etc., may be utilized to interact with the smarthelmet. Thus, the rider may not need to utilize their hands, which maybe holding onto handle bars, to operate the system. The motionmeasurements from the motorcycle may be used to compensate for themotion measurements from the riders' head, thus allowing us to useexisting gesture recognition modules.

The disclosure below describes a system that enables a rider to interactwith a smart helmet based on head gestures. The system uses sensors onthe smart helmet in conjunction with the sensors on the motorcycle torobustly detect gestures. The detected gestures are used to control thedisplay layout on the smart helmet. Common head gestures such asnodding, pitching, lookup, yaw, head tilt is used to interact with thesmart helmet. These gestures can be performed while the rider maintainsattention on the road. Since these interactions are natural, they can beperformed while using the intercom.

Gesture recognition may be performed using measurements either from avisual cameras or an inertial measurement units (IMU) attached to thehelmet. An IMU produces a time series measurement of the linearacceleration and rotational rate. An end-to end method can learn torecognize gestures based on raw measurements from the IMU. However, inorder to robustly recognize gestures in dynamic conditions caused by thevehicle motion, the system may need to sample gestures while the rideris experiencing vehicle motions. In order to perform robust headgestures recognition, the system may need to collect gestures while thevehicle experiences motion, such as wheel jerks, vehicle tilt, etc.

As disclosed below, the system may utilize an IMU on the motorcycle inaddition to the IMU measurements from the helmet to perform robustgesture recognition. The system may be supported with visual camerameasurements. The motorcycle motion estimated based on the IMUobservations is used to stabilize the IMU motion from the helmet. Thismay enable gesture recognition trained on riders while stationary to beused in moving conditions. Though one embodiment may not utilize visualmeasurements from a camera, the same approach can be extended to useadditional sensors, such as cameras.

FIG. 1 is an example of a system design 100 that includes a smart helmet101 and a motorcycle 103. The smart helmet 101 and motorcycle 103 mayinclude various components and sensors that interact with each other.The smart helmet 101 may focus on collecting data related to body andhead movement of a driver. In one example, the smart helmet 101 mayinclude a camera 102. The camera 102 of the helmet 101 may include aprimary sensor that is utilizing for position and orientationrecognition in moving vehicles. Thus, the camera 102 may face outside ofthe helmet 101 to track other vehicles and objects surrounding a rider.The camera 102 may have difficulty capturing dynamics of such objectsand vehicles. In another example, the helmet 101 may be included withradar or LIDAR sensors, in addition to or instead of the camera 102.

The helmet 101 may also include a helmet inertial measurement unit (IMU)104. The helmet IMU 104 may be utilized to track high dynamic motion ofa rider's head. Thus, the helmet IMU 104 may be utilized to track thedirection a rider is facing or the rider viewing direction.Additionally, the helmet IMU 104 may be utilized for tracking suddenmovements and other movements that may arise. An IMU may include one ormore motion sensors.

An Inertial Measurement Unit (IMU) may measure and report a body'sspecific force, angular rate, and sometimes the earth's magnetic field,using a combination of accelerometers and gyroscopes, sometimes alsomagnetometers. IMUs are typically used to maneuver aircraft, includingunmanned aerial vehicles (UAVs), among many others, and spacecraft,including satellites and landers. The IMU may be utilized as a componentof inertial navigation systems used in various vehicle systems. The datacollected from the IMU's sensors may allow a computer to track a motorposition.

An IMU may work by detecting the current rate of acceleration using oneor more axes, and detect changes in rotational attributes like pitch,roll and yaw using one or more axes. Typical IMU also includes amagnetometer, which may be used to assist calibration againstorientation drift by using earth's magnetic field measurements. Inertialnavigation systems contain IMUs that have angular and linearaccelerometers (for changes in position); some IMUs include a gyroscopicelement (for maintaining an absolute angular reference). Angular ratemeters measure how a vehicle may be rotating in space. There may be atleast one sensor for each of the three axes: pitch (nose up and down),yaw (nose left and right) and roll (clockwise or counter-clockwise fromthe cockpit). Linear accelerometers may measure non-gravitationalaccelerations of the vehicle. Since it may move in three axes (up &down, left & right, forward & back), there may be a linear accelerometerfor each axis. The three gyroscopes are commonly placed in a similarorthogonal pattern, measuring rotational position in reference to anarbitrarily chosen coordinate system. A computer may continuallycalculate the vehicle's current position. For each of the six degrees offreedom (x,y,z and Ox, Oy, and Oz), it may integrate over time thesensed acceleration, together with an estimate of gravity, to calculatethe current velocity. It may also integrate the velocity to calculatethe current position. Some of the measurements provided by an IMU arebelow:

â _(B) =R _(BW)(a _(w) −g _(w))+b _(a)+η_(a)

{circumflex over (ω)}_(B)=ω_(B) +b _(g)+η_(g)

(â_(B), {circumflex over (ω)}_(B)) are the raw measurements from the IMUin the body frame of the IMU. a_(w), ω_(B) are the expected correctacceleration and the gyroscope rate measurements. b_(a), b_(g) are thebias offsets in accelerometer and the gyroscope. η_(a), η_(g) are thenoises in accelerometer and the gyroscope.

The helmet 101 may also include an eye tracker 106. The eye tracker 106may be utilized to determine a direction of where a rider of themotorcycle 103 is looking. The eye tracker 106 can also be utilized toidentify drowsiness and tiredness or a rider of the PTW. The eye tracker106 may identify various parts of the eye (e.g. retina, cornea, etc.) todetermine where a user is glancing. The eye tracker 106 may include acamera or other sensor to aid in tracking eye movement of a rider.

The helmet 101 may also include a helmet processor 108. The helmetprocessor 107 may be utilized for sensor fusion of data collected by thevarious camera and sensors of both the motorcycle 103 and helmet 101. Inother embodiment, the helmet may include one or more transceivers thatare utilized for short-range communication and long-range communication.Short-range communication of the helmet may include communication withthe motorcycle 103, or other vehicles and objects nearby. In anotherembodiment, long-range communication may include communicating to anoff-board server, the Internet, “cloud,” cellular communication, etc.The helmet 101 and motorcycle 103 may communicate with each otherutilizing wireless protocols implemented by a transceiver located onboth the helmet 101 and motorcycle 103. Such protocols may includeBluetooth, Wi-Fi, etc. The helmet 101 may also include a heads-updisplay (HUD) that is utilized to output graphical images on a visor ofthe helmet 101.

The motorcycle 103 may include a forward-facing camera 105. Theforward-facing camera 105 may be located on a headlamp or other similararea of the motorcycle 103. The forward-facing camera 105 may beutilized to help identify where the PTW is heading. Furthermore, theforward-facing camera 105 may identify various objects or vehicles aheadof the motorcycle 103. The forward-facing camera 105 may thus aid invarious safety systems, such as an intelligent cruise control orcollision-detection systems.

The motorcycle 103 may include a bike IMU 107. The bike IMU 107 may beattached to a headlight or other similar area of the PTW. The bike IMU107 may collect inertial data that may be utilized to understandmovement of the bike. The bike IMU 107 have multiple axis accelerometer,typically in three orthogonal axes. Similarly, the bike IMU 107 may alsoinclude multiple gyroscopes.

The motorcycle 103 may include a rider camera 109. The rider camera 109may be utilized to keep track of a rider of the motorcycle 103. Therider camera 109 may be mounted in various locations along a handlebarof the motorcycle, or other locations to face the rider. The ridercamera 109 may be utilized to capture images or video of the rider thatare in turn utilized for various calculations, such as identifyingvarious body parts or movement of the rider. The rider camera 109 mayalso be utilized to focus on the eye's of the rider. As such, eye gazemovement may be determined to figure out where the rider is looking.

The motorcycle 103 may include an electronic control unit 111. The ECU111 may be utilized to process data collected by sensors on themotorcycle, as well as data collected by sensors on the helmet. The ECU111 may utilize the data received from the various IMUs and cameras toprocess and calculate various positions or to conduct objectrecognition. The ECU 111 may be in communication with the rider camera109, as well as the forward-facing camera 105. For example, the datafrom the IMUs may be fed to the ECU 111 to identify position relative toa reference point, as well as orientation. When image data is combinedwith such calculations, the bike's movement can be utilized to identifythe direction a rider is facing or focusing on. The image data from boththe forward facing camera on the bike and the camera on the helmet arecompared to determine the relative orientation between the bike and theriders head. The image comparison can be performed based on sparsefeatures extracted from both the cameras (e.g., rider camera 109 andforward-facing camera 105). The motorcycle 103 may include a bikecentral processing unit 113 to support the ECU. The system may thuscontinuously monitor the rider attention, posture, position,orientation, contacts (e.g., grip on handlebars), rider slip (e.g.,contact between rider and seat), rider to vehicle relation, and rider toworld relation.

FIG. 2 illustrates a block diagram of a system for gesture interactionon a smart helmet. In order to improve the robustness of the gesturerecognition system and to compensate for the vehicle motion, the systemmay use the measurements from the bike IMU 203. However, themeasurements from the motorcycle IMU 203 attached to the motorcycle(z¬B(tB)) (e.g., ride movement of the bike at a certain point in time)and the IMU attached to the helmet (zH(tH)) (e.g., ride movement of thehelmet at a certain point in time) are captured at different timeinstances on devices with different clock domains. Hence, in thesynchronization block timestamps data received from the helmet and themotorcycle are compared to perform clock domain synchronization.Utilizing the result of the synchronization, the IMU measurements fromthe motorcycle (e.g., bike IMU) may be time-shifted to the helmet clockdomain. The output of the synchronization block may be time seriessynchronized motion data (IMU measurements) from the helmet and themotorcycle.

f:{zB(tB),t¬H}−>zB(tH)

The synchronization block 205 may include software that is utilized tosynchronize all data that is being collected to help identify thegesture, including image data as well as other sensor data. Thesynchronization block 205 may not only collect the motion data from thehelmet IMU 201 and the bike IMU 203, but it may also utilize image datathat is collected from cameras located on the motorcycle or on thehelmet. Thus, the camera data (e.g., image data) may also be utilized tohelp identify if a gesture is being made. For example, the camera maymonitor a steady state position for the rider during normal ridingperiods, however, may include a detection mechanism to understand whenthe rider's helmet moves a certain way up or down.

The measured accelerations and rotational rates from the IMU on thehelmet (e.g., helmet IMU 201) may need to be compensated by removing theeffect of the vehicle motion. For example, the system may need tounderstand the riding environment in order to understand when the useris providing a gesture and differentiate the gesture from head movementthat has occurred in response to road bumps or other natural ridingoccurrences. The system may remove the effect of the vehicle motion byusing the time synchronized measurements from the motorcycle (e.g., bikeIMU, bike camera etc 203). The time synchronized block may compare datafrom the helmet and the motorcycle at specific times to see if there iscorrelation that caused by the vehicle environment versus a gesture. Arelative rigid body transformation between the vehicle and the helmetIMU (THB(t)) is used to transform the measurements from the motorcycleIMU into the coordinate frame of the helmet IMU. The transformed valuemay be subtracted to estimate the compensated helmet measurements(zc(t)). The rigid body transformation is time dependent and needs to beestimated continuously. A non-linear filter may be utilized to minimizesthe error between predicted pose of the helmet and the pose observed bythe integrating helmet IMU measurements. The predicted pose of thehelmet is derived using the based on a human skeleton model. The resultof the non-linear filter is the relative transformation THB(t).

zc(tH)=zH(tH)θTHB(tH)zB(tH)

A gesture recognizer 209 may include software for gesture recognition.The gesture recognition may be performed by comparing the currentcompensated helmet IMU measurement sequence over a window w, z¬¬c(tH−w:tH+w) with a database of helmet IMU motion sequence for each gesture.The window may be a time period to compare data points from varioustimes across a set amount of time. Various gestures, such as the nod,yaw, lookup, tilt, pitch, stationary gestures may be pre-trained instatic environment. Thus, the system may include an option to activate alearning mode that asks a user to imitate such gestures (the nod, yaw,lookup, tilt, pitch, stationary gestures) to record the IMU data as abaseline when the motorcycle is in the static environment. Each gesturemay be encoded using different internal representation. Thus, each ofthe gestures may have an associated IMU measurement over a time periodto associate with that gesture. The gesture recognizer may provide alikelihood for each class of gesture during runtime. Bayesian learning,deep learning or other learning approaches can be used to create alearnt gesture recognizer model. Inferences can be made on this model todetect and identify the gesture as associated likelihoods. The classwith the highest likelihood is selected as output. The output maycommand an action that is associated with each gesture. For example, atilt left or tilt right may be associated with activating a turn signal.The helmet may send such data wirelessly to the motorcycle to requestthe motorcycle to initiate the command. A filter based on history ofdetector outputs is used to reduce noise and perform outlier detections.

The event manager 211 may synchronize the output from the rider'sgesture recognition block with the information from the bike ECU 213.The bike ECU 213 may be utilized to aggregate messages from thedifferent parts of the motorcycle, as well as to collect data and otherinformation from other sensors on the bike. The event manager maycontain trigger messages for notifications, alerts, and vehicleinformation that are displayed to the rider or provided using audiosound output. The event manager 211 may use a finite state machine whosetransitions are determined by the trigger message from the ECU and therecognized gestures. Thus, a different gesture may be timed based on themessages retrieved from the event manager from the bike, helmet, oroff-board server. Thus, a gesture of a nod from the helmet may includedifferent output commands when a low fuel warning is present, versuswhen the motorcycle is utilizing cruise control. Similarly if the rideris using the intercom, gesture operations can be used to control theintercom, which in other conditions might be used for playingaudio/music.

The event manager 211 may drive or operate the display controller 215.The display controller 215 may contain information about the layout fordifferent information. For example, the display controller 215 maydecide what status information is shown on the helmet display and alsothe manner in which it has to be provided. Furthermore, the displaycontroller 215 may also store the graphical user interface (GUI)elements and animations displayed on the smart helmet display. Thecontrol and navigation GUI elements may be changed based on the inputsfrom the gesture recognizer. For example, a nod left or right utilizingthe smart helmet may change the navigation display screen that isdisplayed on the smart helmet, or change modes from audio mode tonavigation mode, for example. The display controller 215 may alsocontain a profile of layouts for different riding scenarios which can bechanged utilizing gestures. For example, the layouts may includeinterfaces for audio screens, hands free communication, vehicleinformation (e.g., speed, engine revolutions, tire pressure,temperature, fuel economy, fuel mileage, fuel level, etc.), navigationinformation, etc.

The smart helmet display 217 may be utilized to output objects on a HUD.The helmet can include, but is not limited to, a front facing camera, arear facing camera, an accelerometer, a volatile memory, a non-volatilememory, a power source, a data connector, a HUD, a Global PositioningSystem (GPS), a processor, a network transceiver, a Bluetoothtransceiver, and the like. In the embodiment, the electronic componentscooperate to perform a variety of tasks including, but not limited to,displaying vehicle instrumentation information, automatically performingemergency procedures (e.g., calling an EMS), presenting pre-collisionwarning, recording sensor information (e.g., IMU data), etc. The smartdisplay 217 may include a HUD that can be a transparent display whichcan presents data without requiring the rider to look away from aselected viewpoint. The smart display 217 can be presented within avisor (e.g., embedded display), presented within a display proximate tohelmet, and the like. Display 217 technology can include liquid crystaldisplay (LCD), liquid crystal on silicon (LCoS), digital micro-mirrors(DMD), organic light-emitting diode (OLED), optical waveguide, scanninglaser, and the like. Display 217 can present relevant information inreal-time or near real-time.

FIG. 3 is an example flow chart of a smart helmet that allows gesturerecognition. At step 301, the system may collect both the helmet dataand bike data. Such data may include IMU and image data from the helmet,as well as from the motorcycle or power two-wheeled vehicle. The systemmay aggregate the data to later utilize the data to determine if agesture has been commanded by the user, or to determine how tocompensate for the movement of the motorcycle to identify a gesture.Thus, the system will want to avoid identifying a gesture during aninadvertent condition when the road conditions or movement of themotorcycle causes jerking or other sudden movement of the helmet. Asexplained, the motorcycle movement data collected by the motorcycle IMUand/or the image data may be utilized to compensate for the bike'smovement.

At step 303, the system may analyze the helmet data and motorcycle datathat is collected from the various sensors, cameras, and othercomponents. The system may analyze the data either offline at a remoteserver (e.g., the cloud), at the helmet via the helmet's processor, orat the motorcycle via the motorcycle's processor. From there, the systemmay collect movement data of both the helmet and motorcycle to determineif the user intentionally attempted to create a gesture to activate acommand or action. The analysis may include compensation for vehiclemovement, as discussed above with respect to FIG. 2.

At step 305, the system may determine if the gesture has beenrecognized. Thus, the movement may match or come close to matching abaseline gesture measurement, or exceed a movement threshold to beidentified as a gesture. The system may include baseline measurementsfor each gesture based on the helmet IMU. The system may also utilizecamera data from a motorcycle or on the helmet to help identify thegesture utilizing images that may be compared to the gesture.Furthermore, it may compensate for any movement from the motorcycle asshown by the IMU.

At step 307, the system may identify the gesture and associated commandrelated to the gesture. The system may have a look up table that storeseach associated command as related to each gesture. For example, a headtilt right may activate a right turn signal, and a head tilt left mayactivate a left turn signal. The look up table may include informationas to what command must be sent to which controller. Furthermore, thelook-up table may map also different commands for different gesturesbased on the display interface of the smart display. For example, a headnod during an audio mode may change tracks, but a head nod during ahands-free conversation may change call volume.

At step 309, the system may then output the command associated with thegesture. The commands include having a processor execute commandsrelated to the gesture, followed by displaying any relevant informationon the display of the smart helmet. The commands may be related tofeatures on the vehicle helmet or on the motorcycle itself. Furthermore,the commands may require wireless data or other information to beexchanged between the helmet, bike, off-board servers, other bikes, etc.Information may also be displayed on an instrument cluster of themotorcycle to identify executing of the command or to update a status.

The processes, methods, or algorithms disclosed herein can bedeliverable to/implemented by a processing device, controller, orcomputer, which can include any existing programmable electronic controlunit or dedicated electronic control unit. Similarly, the processes,methods, or algorithms can be stored as data and instructions executableby a controller or computer in many forms including, but not limited to,information permanently stored on non-writable storage media such as ROMdevices and information alterably stored on writeable storage media suchas floppy disks, magnetic tapes, CDs, RAM devices, and other magneticand optical media. The processes, methods, or algorithms can also beimplemented in a software executable object. Alternatively, theprocesses, methods, or algorithms can be embodied in whole or in partusing suitable hardware components, such as Application SpecificIntegrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs),state machines, controllers or other hardware components or devices, ora combination of hardware, software and firmware components.

While exemplary embodiments are described above, it is not intended thatthese embodiments describe all possible forms encompassed by the claims.The words used in the specification are words of description rather thanlimitation, and it is understood that various changes can be madewithout departing from the spirit and scope of the disclosure. Aspreviously described, the features of various embodiments can becombined to form further embodiments of the invention that may not beexplicitly described or illustrated. While various embodiments couldhave been described as providing advantages or being preferred overother embodiments or prior art implementations with respect to one ormore desired characteristics, those of ordinary skill in the artrecognize that one or more features or characteristics can becompromised to achieve desired overall system attributes, which dependon the specific application and implementation. These attributes caninclude, but are not limited to cost, strength, durability, life cyclecost, marketability, appearance, packaging, size, serviceability,weight, manufacturability, ease of assembly, etc. As such, to the extentany embodiments are described as less desirable than other embodimentsor prior art implementations with respect to one or morecharacteristics, these embodiments are not outside the scope of thedisclosure and can be desirable for particular applications.

1. A helmet, comprising: a transceiver configured to receive vehicledata from one or more sensors located on a vehicle, wherein the vehicledata includes image data associated with a rider-facing camera of amotorcycle; an inertial measurement unit (IMU) configured to collectmotion data of the helmet associated with a rider of the vehicle; and aprocessor in communication with the transceiver and IMU, and programmedto: receive, via the transceiver, vehicle data from the one or moresensors located on the vehicle; determine a gesture in response to thevehicle data from the one or more sensors located on the vehicle and thehelmet motion data from the IMU, wherein the processor is furtherprogrammed to compensate vehicle motion utilizing the vehicle data indetermining the gesture; and output on a display of the helmet a statusinterface related to the vehicle, in response to the gesture.
 2. Thehelmet of claim 1, wherein the vehicle data and helmet motion data eachincludes time stamps identifying when the vehicle data or helmet motiondata were collected, wherein the time stamps are utilized to timesynchronize measurements from the vehicle to the helmet.
 3. (canceled)4. The helmet of claim 1, wherein the processor is further programmed tocompensate the helmet motion data utilizing at least the vehicle data.5. The helmet of claim 1, wherein the processor is further programmed tocompensate utilizing time stamps from the vehicle data and helmet motiondata.
 6. The helmet of claim 1, wherein the processor is furtherconfigured to determine the gesture in response to a nod, yaw, lookup,tilt, or pitch.
 7. The helmet of claim 1, wherein the helmet includes aheads-up display configured to output graphical images on a visor of thehelmet.
 8. The helmet of claim 1, wherein the helmet includes a camera.9. A system that includes a helmet and a vehicle with at least twowheels, comprising: a helmet transceiver configured to receive vehicledata from one or more sensors located on the vehicle; a helmet inertialmeasurement unit (IMU) configured to collect helmet motion dataassociated with the helmet; a rider-facing camera located on the vehicleand configured to monitor a rider of the vehicle and collect rider imagedata; and a processor in the helmet in communication with the helmettransceiver and helmet IMU, and programmed to: receive, via thetransceiver, gesture data from the helmet transceiver and vehicle motiondata from a vehicle IMU; determine a gesture in response to the gesturedata and compensating the helmet motion data with the vehicle data,wherein the processor is further programmed to compensate vehicle motionutilizing the vehicle data in determining the gesture; and output on adisplay of a helmet an action in response to the gesture.
 10. The systemof claim 9, wherein the processor in the helmet is further programmed toactivate a learning mode to identify one or more gestures in a staticvehicle environment when the vehicle is not in operation.
 11. The systemof claim 9, wherein the display of the helmet includes a heads-updisplay configured to output graphical images on a visor of the helmet.12. The system of claim 9, wherein the vehicle is a motorcycle or apowered two-wheel unit.
 13. The system of claim 9, wherein the processoris further configured to determine the gesture utilizing at least therider image data.
 14. The system of claim 9, wherein the vehicle dataincludes vehicle motion data collected from a vehicle inertialmeasurement unit.
 15. A helmet, comprising: a transceiver configured toreceive vehicle data from one or more sensors located on a vehicle,including image data proximate to a rider of the vehicle, wherein theimage data is from a rider-facing camera of the vehicle; an inertialmeasurement unit (IMU) configured to collect helmet motion data of thehelmet associated with a rider of the vehicle; and a processor incommunication with the transceiver and IMU, and programmed to: receive,via the transceiver, vehicle data from the one or more sensors locatedon the vehicle; and determine a gesture in response to the vehicle datafrom the one or more sensors located on the vehicle and the helmetmotion data from the IMU, wherein the processor is further programmed tocompensate vehicle motion utilizing the vehicle data in determining thegesture; and execute a command in response to the gesture.
 16. Thehelmet of claim 15, wherein the processor is further programmed tooutput a status of the vehicle on a display of the helmet in response tothe gesture.
 17. (canceled)
 18. The helmet of claim 16, wherein theprocessor is further programmed to execute the command to activate afunction at the vehicle based upon the gesture.
 19. The helmet of claim17, wherein the processor is further programmed to send the command tothe vehicle via the transceiver.
 20. The helmet of claim 17, wherein theprocessor is further programmed to activate a learning mode to identifyone or more gestures in a static vehicle environment when the vehicle isnot in operation.
 21. The helmet of claim 1, wherein the processor isfurther programmed to remove an effect of vehicle motion by utilizingone or more time synchronized measurements from the vehicle.
 22. Thehelmet of claim 1, wherein the processor is further programmed todetermine the gesture by comparing a current compensated helmet IMUmeasurement sequence over a window w, z¬¬c(tH−w: tH+w) with a databaseof helmet IMU motion sequence for each gesture.